Inspired by [1]}, [2]}, we employ a pyramid FRN to refine the coarse flows and the occlusion mask.
The structure of the \(l\) -th flow refinement block is shown in Fig. REF . Firstly, we use the upsampled previous flows to warp the pyramid features and we get the \(l\) -th warped features \(F_{0,w}^l\)  and \(F_{1,w}^l\) . Then the warped features are fed into convolutional layers to update the occlusion mask \(M^l\) , which is also used to produce a temporary intermediate feature \(F_t^l\) . These operations can be represented as
\(M^l = M^{l-1} + \mathcal {T}_{\sigma }(\mathcal {T}_{occ}([F_{0,w}^l, F_{1,w}^l])),\) 
\(F_t^l = M^l\odot F_{0,w}^l + (1-M^l) \odot F_{1,w}^l,\) 
